Question

Suppose we train a hard-margin linear SVM on n > 100 datapoints in R₂, yielding a hyperplane with exactly 2 support vectors. If we add one more datapoint and retrain the classifier, what is the maximum possible number of support vectors for the new hyperplane (assuming the n + 1 points are linearly separable)? Select one of: {2, 3, n, n + 1}. Optional: draw a case that justifies your answer

Answer 1

The maximum possible number of support vectors for the new hyperplane is n + 1.

Step-by-step explanation:

When training a hard-margin linear SVM on n > 100 datapoints in R₂, the maximum number of support vectors for the hyperplane is n + 1. This means that if we add one more datapoint and retrain the classifier, the maximum possible number of support vectors for the new hyperplane is n + 1. In this case, since n > 100, the maximum possible number of support vectors for the new hyperplane would be greater than 100.

Answer 2

Answer:It is option one

Step-by-step explanation:It jusyt is